Sustainable Ocean Development Requires All Hands On Deck on Conservation, Improvement, and Restoration of Ecosystems and Biodiversity
Bibliographic record
Abstract
Implementation of the Kunming-Montreal Global Biodiversity Framework (GBF), the Biodiversity Beyond National Jurisdiction Agreement (BBNJ), and the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) requires that different groups coordinate on sharing biodiversity, environmental, and social and economic information, and management goals and practices. These and many other treaties and agreements have much overlap between them in the need for interoperable observations on marine life and biodiversity. Many are directly relevant to plans that nations would be interested in implementing internally for biodiversity monitoring. Can the international community make progress on these agreements? One possible mechanism is to make concerted investments to link what currently represent separate networks of marine researchers, social scientists, technologists, private entities and investors, policymakers, and indigenous and other local groups. These groups shared an interest in the best possible planning and implementation of policy frameworks to advance jobs and the economy while conserving spaces and other resources. They share an interest in contributing to international conventions like the GBF, BBNJ, and CCAMLR. The UN Decade of Ocean Science for Sustainable Development (the Ocean Decade) provides an opportunity to link existing networks to converge on key science and technology requirements, enable minimal key dataflows, and inform policy for sustainable development. Networking around best practices for marine life observing is already occurring in a collaboration between the Global Ocean Observing System (GOOS), the Ocean Biodiversity Information System (OBIS), and the Marine Biodiversity Observation Network (MBON). These and other partners are focused on leading Ocean Decade Programmes to address these challenges. The Ocean Decade could be a good convening mechanism for different interest groups to converge on: A) Common and interoperable practices for the collection and curation of specific sets of biology and ecosystem information; B) Implementing the dataflow strategies required to address big science questions and management of human activities to ensure sustainable development; and C) support local and national-scale capacity building, in partnership and coordination around the world. Such partnerships and collaboration are essential for all nations to advance and achieve some measure of success with the Sustainable Development Goals, and require investments to support existing marine biodiversity observation programs and to identify and fill gaps. Imagining a positive future can incentivize an all-hands-on-deck effort to ensure a better future for ourselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".